Security systems using infrared (IR) cameras have been successfully employed for many years. When the first images produced in total darkness were demonstrated, the application as a night vision security device was obvious to all. This ability to see at night under unlighted conditions has proved quite useful. Early systems were based upon a single camera, and the detection capability was good. They became more effective when they were combined with a trained operator to interpret the image scene. The recent trend has been toward greater automation of these systems using artificial intelligence; and employing expensive high-resolution cameras to provide greater image detail to analyze potential threats.
Today, IR security systems employ image-processing software to automatically detect targets. These sophisticated algorithms discriminate between true and false targets by combining low-resolution motion detection with high-resolution shape recognition. High-resolution IR cameras are very expensive and cost prohibitive for many applications. The results have been good, but needed improvements in false alarm detection have been disappointing. In spite of the progress, these techniques are too expensive and have been unable to replace the human operator.
Security systems using IR technology are powerful because of their ability to detect humans (i.e. warm blooded objects), but they can be fooled by false detections, clutter, or naturally occurring events in the image scene. Performance is further reduced by decreased sensitivity resulting from climatic conditions (i.e. fog, rain, snow, dust, etc.) or excessive range to their targets.
The key to an effective security detection system is measured by two criteria: (1) the probability of detection of a true target when present, and (2) the frequency of false alarms of the system when a true target was not present. The perfect system is 100% probability for (1) and 0% occurrence for (2). One approach has been to make the definition of a target of interest broad enough to ensure all true targets are detected (100%) and to develop algorithms to detect false targets while retaining the true targets. Elaborate sets of spatial, motion, location, and image feature recognition techniques have been tried, and some are in use today. However, there remains much room for improvement.
For the foregoing reasons, there is a clear and long felt need for an IR security system and method that achieves the high-resolution threat discrimination of newer, more expensive IR cameras from less costly, low-resolution IR cameras, while avoiding false alarms due to clutter. Such robust and inexpensive target detection would allow the threat criteria to be expanded into behavioral observations and activities unattainable by current systems. Full automation, resulting in elimination of a dedicated human operator while maintaining high detection probabilities with low false alarm rates, is a need yet to be satisfied.